Data for 'Efficient characterisation of large deviations using population dynamics'

Data for 'Efficient characterisation of large deviations using population dynamics'

We have produced a research paper 'Efficient characterisation of large deviations using population dynamics' investigating the nature of rare events in the SSEP. We study activity distribiutions of this process on a one dimensional lattice with periodic boundary conditions. The process is simulated using a C++ code and parallelisation is used to increase computational efficiency. The two parallelisation methods that we use are OpenMP and MPI and these are stored in a repository in this data set. The codes are designed such that they can be used to study rare events in other processes and study observables other than activity. The dataset includes .txt and .mat files output by the C++ files and by the .m files used for processing. Further .m MATLAB files are used to produce the data and tables within the paper.

Keywords:
Large Deviations, Efficient Computation, Parallelisation, Numerical Algorithm, Population Dynamics
Subjects:

Cite this dataset as:
Brewer, T., Jack, R., 2018. Data for 'Efficient characterisation of large deviations using population dynamics'. University of Bath. https://doi.org/10.15125/BATH-00457.

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Data

Data%20Files.zip
application/zip (33MB)
Creative Commons: Attribution 4.0

Dataset for the research paper 'Efficient characterisation of large deviations using population dynamics'. This research made use of the Balena High Performance Computing (HPC) Service at the University of Bath.

Creators

Tobias Brewer
University of Bath

Robert Jack
University of Bath

Contributors

Stephen Clark
Supervisor
University of Bath

Russell Bradford
Supervisor

University of Bath
Rights Holder

Coverage

Collection date(s):

From 1 October 2015 to 1 October 2017

Documentation

Data collection method:

Running simulations with .cpp and .hpp header files. Producing some analytic values with .m MATLAB files.

Data processing and preparation activities:

Data processed and plotted with .m Matlab files. To run alternative systems or observables the user is required to define classes in header files.

Technical details and requirements:

Computational files: .cpp, .hpp and Matlab files are included in this dataset. The OpenMP and serial codes are run with an intel compiler (icpc) and the MPI codes are run with the corresponding intel MPI compiler (mpiicpc). This research made use of the Balena High Performance Computing (HPC) Service at the University of Bath.

Additional information:

Each data folder is related to a figure within the 'Efficient characterisation of large deviations using population dynamics' paper. Each folder (including the repository) has a README.md file corresponding to the enclosed data and .m processing files. This research made use of the Balena High Performance Computing (HPC) Service at the University of Bath.

Documentation Files

README.md
text/plain (1kB)
Creative Commons: Attribution 4.0

README for the data set including a list of the folders and subfolders within the data set and what type of files each folder and subfolder contain.

Funders

ClusterVision

Efficient simulation of rare events and large deviations in physical systems

Publication details

Publication date: 8 May 2018
by: University of Bath

Version: 1

DOI: https://doi.org/10.15125/BATH-00457

URL for this record: https://researchdata.bath.ac.uk/id/eprint/457

Related articles

Brewer, T., Clark, S. R, Bradford, R. and Jack, R. L, 2018. Efficient characterisation of large deviations using population dynamics. Journal of Statistical Mechanics: Theory and Experiment, 2018(5), p.053204. Available from: https://doi.org/10.1088/1742-5468/aab3ef.

Contact information

Please contact the Research Data Service in the first instance for all matters concerning this item.

Contact person: Tobias Brewer

Departments:

Faculty of Science
Computer Science
Physics